Decision Making under Deep Uncertainty and the Great Acceleration

Seventy-five years into the Great Acceleration—a period marked by unprecedented growth in human activity and its effects on the planet—some type of societal transformation is inevitable. Successfully navigating these tumultuous times requires scientific, evidence-based information as an input into society’s value-laden decisions at all levels and scales. The methods and tools most commonly used to bring such expert knowledge to policy discussions employ predictions of the future, which under the existing conditions of complexity and deep uncertainty can often undermine trust and hinder good decisions. How, then, should experts best inform society’s attempts to navigate when both experts and decisionmakers are sure to be surprised? Decision Making under Deep Uncertainty (DMDU) offers an answer to this question. With its focus on model pluralism, learning, and robust solutions coproduced in a participatory process of deliberation with analysis, DMDU can repair the fractured conversations among policy experts, decisionmakers, and the public. In this paper, the author explores how DMDU can reshape policy analysis to better align with the demands of a rapidly evolving world and offers insights into the roles and opportunities for experts to inform societal debates and actions toward more-desirable futures.